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      Control of the Anterior Pituitary Cell Lineage Regulator POU1F1 by the Stem Cell Determinant Musashi

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          Abstract

          The adipokine leptin regulates energy homeostasis through ubiquitously expressed leptin receptors. Leptin has a number of major signaling targets in the brain, including cells of the anterior pituitary (AP). We have previously reported that mice lacking leptin receptors in AP somatotropes display growth hormone (GH) deficiency, metabolic dysfunction, and adult-onset obesity. Among other targets, leptin signaling promotes increased levels of the pituitary transcription factor POU1F1, which in turn regulates the specification of somatotrope, lactotrope, and thyrotrope cell lineages within the AP. Leptin’s mechanism of action on somatotropes is sex dependent, with females demonstrating posttranscriptional control of Pou1f1 messenger RNA (mRNA) translation. Here, we report that the stem cell marker and mRNA translational control protein, Musashi1, exerts repression of the Pou1f1 mRNA. In female somatotropes, Msi1 mRNA and protein levels are increased in the mouse model that lacks leptin signaling (Gh-CRE Lepr-null), coincident with lack of POU1f1 protein, despite normal levels of Pou1f1 mRNA. Single-cell RNA sequencing of pituitary cells from control female animals indicates that both Msi1 and Pou1f1 mRNAs are expressed in Gh-expressing somatotropes, and immunocytochemistry confirms that Musashi1 protein is present in the somatotrope cell population. We demonstrate that Musashi interacts directly with the Pou1f1 mRNA 3′ untranslated region and exerts translational repression of a Pou1f1 mRNA translation reporter in a leptin-sensitive manner. Musashi immunoprecipitation from whole pituitary reveals coassociated Pou1f1 mRNA. These findings suggest a mechanism in which leptin stimulation is required to reverse Musashi-mediated Pou1f1 mRNA translational control to coordinate AP somatotrope function with metabolic status.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Is Open Access

              Massively parallel digital transcriptional profiling of single cells

              Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Endocrinology
                The Endocrine Society
                0013-7227
                1945-7170
                March 2021
                March 01 2021
                March 2021
                March 01 2021
                December 29 2020
                : 162
                : 3
                Affiliations
                [1 ]Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
                [2 ]Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
                [3 ]Hendrix College, Conway, Arkansas, USA
                [4 ]Memorial Sloan Kettering Cancer Center, New York, New York, USA
                [5 ]University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [6 ]Arkansas Children’s Research Institute, Little Rock, Arkansas, USA
                Article
                10.1210/endocr/bqaa245
                33373440
                55071fba-1d88-4d66-8469-14652a027fbe
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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